WorldPopulationAnalysis2024 is a comprehensive project aimed at analyzing the world population in 2024. It focuses on comparing urban and rural areas regarding population density, growth rates, and access to essential infrastructure services such as electricity, clean water, healthcare, and education. The project leverages advanced data visualization techniques and machine learning models to provide valuable insights into the correlation between population distribution and economic indicators.
- Urban vs. Rural Population Analysis 🏙️🌾: Compare the population sizes and densities of urban and rural areas across various countries.
- Infrastructure Accessibility ⚡🚰🏥📚: Analyze access to electricity, clean water, healthcare, and education in urban and rural regions.
- Economic Indicators Correlation 📊: Study the correlation between population distribution and economic indicators such as GDP, unemployment rates, and education levels.
- Data Visualization 📈: Utilize advanced visualization tools to present data insights in an understandable manner.
- Machine Learning Models 🤖: Implement regression models to predict future trends and analyze relationships between variables.
- Python
- Pandas
- Matplotlib
- Seaborn
- Plotly
- Folium
- Scikit-learn
- Prophet
- World Population by Country 2024: Comprehensive population data for each country.
- Country Coordinates World: Geographic coordinates for each country.
- Economic and Infrastructure Data: Data on GDP, unemployment rates, education levels, and infrastructure access.
- Python 3.11
- Required Python libraries (specified in
requirements.txt)
- Clone the repository:
git clone https://github.com/yourusername/WorldPopulationAnalysis2024.git
- Navigate to the project directory:
cd WorldPopulationAnalysis2024 - Install the required packages:
pip install -r requirements.txt
- Run the Jupyter Notebook:
jupyter notebook
- Open
world_population_analysis.ipynbto explore the analysis and visualizations.
The project provides a comprehensive analysis of how population distribution affects infrastructure access and economic indicators. Key findings include differences in access to essential services between urban and rural areas and the impact of these differences on economic outcomes.
| Region | Country | Population 2024 |
|---|---|---|
| Urban | India | 500,000,000 |
| Urban | China | 800,000,000 |
| Urban | United States | 250,000,000 |
| Urban | Indonesia | 150,000,000 |
| Urban | Pakistan | 90,000,000 |
| Urban | Turkey | 50,000,000 |
| Rural | India | 941,719,852 |
| Rural | China | 625,178,782 |
| Rural | United States | 91,814,420 |
| Rural | Indonesia | 129,798,049 |
| Rural | Pakistan | 155,209,815 |
| Rural | Turkey | 38,510,876 |
| Service | Urban (%) | Rural (%) |
|---|---|---|
| Electricity Access | 96.67 | 75.00 |
| Clean Water Access | 92.83 | 68.33 |
| Healthcare Access | 88.00 | 55.00 |
| Education Access | 87.50 | 62.50 |
| Indicator | Correlation (Urban) | Correlation (Rural) |
|---|---|---|
| GDP (Billion USD) | 0.79 | 0.79 |
| Unemployment Rate (%) | -0.34 | -0.34 |
| Education Level (Mean years) | 1.00 | 1.00 |
Contributions are welcome! Please fork the repository and create a pull request with your changes.
This project is licensed under the MIT License - see the LICENSE file for details.
For more information, please contact Pınar Topuz.